Chun-Ying Liu, Gong-Ping Yang, Yu-Wen Huang, Fu-Xian Huang. Dual-domain and Multiscale Fusion Deep Neural Network for PPG Biometric Recognition[J]. Machine Intelligence Research, 2023, 20(5): 707-715. DOI: 10.1007/s11633-022-1366-8
Citation: Chun-Ying Liu, Gong-Ping Yang, Yu-Wen Huang, Fu-Xian Huang. Dual-domain and Multiscale Fusion Deep Neural Network for PPG Biometric Recognition[J]. Machine Intelligence Research, 2023, 20(5): 707-715. DOI: 10.1007/s11633-022-1366-8

Dual-domain and Multiscale Fusion Deep Neural Network for PPG Biometric Recognition

  • Photoplethysmography (PPG) biometrics have received considerable attention. Although deep learning has achieved good performance for PPG biometrics, several challenges remain open: 1) How to effectively extract the feature fusion representation from time and frequency PPG signals. 2) How to effectively capture a series of PPG signal transition information. 3) How to extract time-varying information from one-dimensional time-frequency sequential data. To address these challenges, we propose a dual-domain and multiscale fusion deep neural network (DMFDNN) for PPG biometric recognition. The DMFDNN is mainly composed of a two-branch deep learning framework for PPG biometrics, which can learn the time-varying and multiscale discriminative features from the time and frequency domains. Meanwhile, we design a multiscale extraction module to capture transition information, which consists of multiple convolution layers with different receptive fields for capturing multiscale transition information. In addition, the dual-domain attention module is proposed to strengthen the domain of greater contributions from time-domain and frequency-domain data for PPG biometrics. Experiments on the four datasets demonstrate that DMFDNN outperforms the state-of-the-art methods for PPG biometrics.
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